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基于双目立体视觉的运动员高强度运动损伤图像识别。

Image Recognition of Sports Athletes' High-Intensity Sports Injuries Based on Binocular Stereo Vision.

机构信息

Xinyang Normal University, Xinyang 464000, China.

出版信息

Comput Intell Neurosci. 2022 Jul 31;2022:4322597. doi: 10.1155/2022/4322597. eCollection 2022.

Abstract

Sports athletes are prone to certain injuries during high-intensity exercise training. In the process of treating an injury, images of the injury site need to be collected and identified. However, the traditional recognition method cannot effectively extract the features of the image. At the same time, it ignores the optimization of the damage image recognition results, resulting in low recognition accuracy and poor efficiency. Binocular stereo vision technology can quickly and accurately detect moving objects. Therefore, in order to more accurately identify high-intensity sports injury images, this study takes the high-intensity sports injury images as the basic research object. Several processes of image processing based on binocular stereo vision are analyzed, and the vulnerable parts of the body in high-intensity sports are also studied. Finally, the method in this study is verified. The experimental results show that the method proposed in this study reduces the average error rate by 0.19% compared with the traditional recognition method. It can effectively identify and detect injury images, thereby improving the accuracy and stability of sports injury image identification. The  identification time is also shortened accordingly, which has certain practicability and feasibility. In addition, the binocular camera used in this study has high accuracy, and the obtained images of sports injuries are of good quality, which lays a foundation for image detection and recognition.

摘要

运动运动员在高强度运动训练中容易受到某些伤害。在治疗损伤的过程中,需要收集和识别损伤部位的图像。然而,传统的识别方法不能有效地提取图像的特征。同时,它忽略了对损伤图像识别结果的优化,导致识别精度低、效率差。双目立体视觉技术可以快速准确地检测运动物体。因此,为了更准确地识别高强度运动损伤图像,本研究以高强度运动损伤图像为基本研究对象。分析了基于双目立体视觉的图像处理的几个过程,并研究了高强度运动中的易损部位。最后,验证了本研究中的方法。实验结果表明,与传统识别方法相比,本研究提出的方法将平均错误率降低了 0.19%。它可以有效地识别和检测损伤图像,从而提高运动损伤图像识别的准确性和稳定性。识别时间也相应缩短,具有一定的实用性和可行性。此外,本研究中使用的双目相机具有很高的精度,获得的运动损伤图像质量良好,为图像检测和识别奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc05/9357738/bc948b2164c0/CIN2022-4322597.001.jpg

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